Develop right‐turn real‐time crash warning system at arterial access considering driver behaviour
Bibliographic record
Abstract
To help drivers safely enter the arterial road from the access road, the authors develop a crash warning system for vehicles in right‐turn scenario based on DSRC (dedicated short range communications). Drivers’ right‐turn behaviours from an access road to an arterial highway are considered in the system. Warning algorithms were tested with field data and DSRC on‐board and roadside equipment. Corresponding outliers filter shows a reliable performance. In this system, right‐turn process is divided into three phases: turn‐in, keep‐steady, and turn‐out. Based on the field data, they establish regression models for each phase. Model results show that: (i) the overall duration of the three phases of the right‐turn manoeuvre increases with the amount of cars coming from left on main artery; (ii) the amount of cars that are influenced by the arterial traffic increases the duration of first two phases, but decreases the last phase duration; (iii) the longer the first two phases last, the shorter the last phase would be; and (iv) drivers tend to decelerate before turning right when there are more than two cars coming from left.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".